Forecasting accuracy evaluation of tourist arrivals

This paper evaluates the use of several parametric and nonparametric forecasting techniques for predicting tourism demand in selected European countries. We find that no single model can provide the best forecasts for any of the countries in the short-, medium- and long-run. The results, which are tested for statistical significance, enable forecasters to choose the most suitable model (from those evaluated here) based on the country and horizon for forecasting tourism demand. Should a single model be of interest, then, across all selected countries and horizons the Recurrent Singular Spectrum Analysis model is found to be the most efficient based on lowest overall forecasting error. Neural Networks and ARFIMA are found to be the worst performing models.     

A SURVEY OF THE ECONOMICS OF PATENT SYSTEMS AND PROCEDURES

The last several decades have seen increases in patenting activity worldwide, as well as growing issues related to patent quality. In response to these quality issues a recent patent literature has emerged, that investigates the behavior and incentives of patent examiners, applicants, and third parties. In this paper, we provide an overview of patent procedures, patent systems and a survey of the new economic literature on patent systems. Both theoretical and empirical papers are considered. Policy implications coming from this literature are presented.     

How to overcome the implementation gap in ecosystem services? A user-friendly and inclusive tool for improved urban management

One of the main issues with the concept of ecosystem services is its absence on the ground in concrete operational decision-making contexts; that is, an implementation gap. In this study, we investigated if this gap could be overcome through the use of open-source data and free tools, and the adoption of a short-term participatory process. We tested these methods in the context of a project in the urban metropolitan area of Bordeaux (Communauté urbaine de Bordeaux: CUB) in France. The ecosystem services were defined using a participatory approach involving local stakeholders, and then selected scenarios were simulated to test the impact of various development or conservation plans. The study addressed three main questions: (1) Is it possible to adopt a simple methodological approach that overcomes the implementation gap through the development of a user-friendly and inclusive method? (2) What is the added value of a participatory approach? (3) With regards to four scenarios in this territory, what are the ES trends in the selected biophysical and monetary indicators, and will knowledge of these trends help planners to shape a sustainable trajectory for the territory?     

Forecasting China's foreign exchange reserves using dynamic model averaging: The roles of macroeconomic fundamentals,financial stress and economic uncertainty

We develop models for examining possible predictors of growth of China's foreign exchange reserves that embrace Chinese and global trade, financial and risk (uncertainty) factors. Specifically, by comparing with other alternative models, we show that the dynamic model averaging (DMA) and dynamic model selection (DMS) models outperform not only linear models (such as random walk, recursive OLS-AR(1) models, recursive OLS with all predictive variables models) but also the Bayesian model averaging (BMA) model for examining possible predictors of growth of those reserves. The DMS is the best overall across all forecast horizons. While some predictors matter more than others over the forecast horizons, there are few that stand the test of time. The US–China interest rate differential has a superior predictive power among the 13 predictors considered, followed by the nominal effective exchange rate and the interest rate spread for most of the forecast horizons. The relative predictive prowess of the oil and copper prices alternates, depending on the commodity cycles. Policy implications are also provided.     

Forecasting macroeconomic variables in emerging economies

Forecasting macroeconomic variables in rapidly changing emerging economies presents a number of challenges. In addition to structural changes, the time-series data are usually available only for a short number of periods, and predictors are available in different lengths and frequencies. Dynamic model averaging (DMA), by allowing the forecasting model to change dynamically over time, permits the use of predictors with different lengths and frequencies for the purpose of forecasting in a rapidly changing economy. This study uses DMA to forecast inflation and growth in Vietnam, Thailand, Philippines, Sri Lanka and Ghana. We compare its forecasting performance with a wide range of other time-series methods. We find that the size and composition of the optimal predictor set changed, indicating changes in the economic relationships over time. We also find that DMA frequently produces more accurate forecasts than other forecasting methods for both inflation and economic growth in the countries studied.     

A critical overview of privacy-preserving approaches for collaborative forecasting

Cooperation between different data owners may lead to an improvement in forecast quality—for instance, by benefiting from spatiotemporal dependencies in geographically distributed time series. Due to business competitive factors and personal data protection concerns, however, said data owners might be unwilling to share their data. Interest in collaborative privacy-preserving forecasting is thus increasing. This paper analyzes the state-of-the-art and unveils several shortcomings of existing methods in guaranteeing data privacy when employing vector autoregressive models. The methods are divided into three groups: data transformation, secure multi-party computations, and decomposition methods. The analysis shows that state-of-the-art techniques have limitations in preserving data privacy, such as (i) the necessary trade-off between privacy and forecasting accuracy, empirically evaluated through simulations and real-world experiments based on solar data; and (ii) iterative model fitting processes, which reveal data after a number of iterations.     

建筑工程高支模板支架搭设施工工艺要点浅析

对于建筑工程项目来讲,高支模施工技术是应用较为频繁的技术形式,因此必须要结合项目实际情况,加强高支模板支架搭设工作。文章就结合具体工程项目分析建筑工程高支模板支架搭设施工工艺要点。     

基于SVM的房地产财务决策风险预测及敏感性分析

本文以房地产上市公司的财务数据为参考依据,结合相关性分析,筛选并建立影响财务风险的指标体系;利用支持向量机建立财务风险分析预测模型;并使用灰色关联理论对指标进行敏感性分析,从而得到影响财务风险最敏感的指标。     

Linking customisation of ERP systems to support effort: an empirical study

The amount of customisation to an enterprise resource planning (ERP) system has always been a major concern in the context of the implementation. This article focuses on the phase of maintenance and presents an empirical study about the relationship between the amount of customising and the resulting support effort. We establish a structural equation modelling model that explains support effort using customisation effort, organisational characteristics and scope of implementation. The findings using data from an ERP provider show that there is a statistically significant effect: with an increasing amount of customisation, the quantity of telephone calls to support increases, as well as the duration of each call.     

Towards a resilience management framework for complex enterprise systems upgrade implementation

The lack of knowledge of how resilience management supports enterprise system (ES) projects accounts for the failure of firms to leverage their investments in costly ES implementations. Using a structured-pragmatic-situational (SPS) case study research approach, this paper reports on an investigation into the resilience management of a large utility company as it implemented an ES upgrade. Drawing on the literature and on the case study findings, we developed a process-based resilience management framework that involves three strategies (developing situation awareness, demystifying threats, and executing restoration plans) and four organisational capabilities that transform resilience management concepts into practices. We identified the crucial phases of ES upgrade implementation and developed indicators for how different strategies and capabilities of resilience management can assist managers at different stages of an ES upgrade. This research advances the state of existing knowledge by providing specific and verifiable propositions for attaining a state of resilience, the knowledge being grounded in the empirical reality of a case study. Moreover, the framework offers ES practitioners a roadmap to better identify appropriate responses and levels of preparedness.